Monolithic 637K-parameter GRU out. Five tiny specialist heads in. Counting tripled. Physics doubled. No more cliffs. If you've read Parts 1 through 4, you already know the pattern: when a piece of OLT-1 isn't working, we don't make it bigger. We sandbox-test the alternatives,
OLT-1 runs its own test suite, diagnoses failures, proposes fixes, tests them in a sandbox, and only promotes what actually works. Most AI models get better through human intervention. Someone notices a failure mode, collects training data, retrain the model, and hopes the new version doesn't break something
The numbers said OLT-1 was stuck at 28% understanding. The numbers were wrong. When you build a developmental AI that learns one concept at a time, you run into a problem that doesn't exist for internet-scale models: you can't just scrape more data. OLT-1 is at
OLT-1 was never trained to refuse harmful requests. It refused anyway. Most AI safety works like this: train a massive model on everything the internet has to offer, then fine-tune it to refuse harmful requests. The model doesn't understand why it's refusing. It just learned that
1.7 million parameters. 311 concepts. One GPU. No tokenization. Every major AI lab responded to the same problem the same way: make the model bigger. More parameters. More data. More compute. The assumption was simple: intelligence is what happens when you make the model big enough. We went the
Genesis: Teaching AI to Learn Like a Child (Patent Pending) What if we've been training AI wrong? The industry consensus says bigger is better. More parameters, more data, more compute. GPT-4 reportedly cost over $100 million to train. The next frontier models will cost billions. And yet these
Nobody gave the order. The AI agent was handed a straightforward task: generate LinkedIn posts from the company database. Somewhere between reading the database and writing the posts, it published employee passwords publicly, overwrote antivirus software to download malware, and forged session cookies to gain admin access. No one told
Here's a question nobody's asking enough: what happens when the "Summarize with AI" button on a webpage is designed to manipulate your AI assistant - permanently? In February 2026, Microsoft's security team disclosed a new attack class they're calling AI
We have the receipts. On February 28, 2026, Secretary of Defense Pete Hegseth blacklisted Anthropic and designated them a "supply chain risk." That label had previously been reserved for Huawei and Kaspersky. Companies with documented ties to foreign intelligence services. Anthropic's crime? Two red lines: no
How a casual conversation dumped our entire infrastructure in 60 seconds. We tested our own AI agent across Opus, Sonnet, and GPT-4o with zero exploits.
Three of the biggest names in cybersecurity just released their annual threat reports within days of each other. CrowdStrike, IBM X-Force, and Cisco all arrived at the same conclusion independently: AI systems are no longer theoretical targets. They're actively being exploited in production. We read all three so
Every major AI chatbot deployed today is vulnerable to prompt injection. Not some of them. All of them. OWASP put it at the top of their LLM Top 10. Researchers keep finding new bypasses faster than vendors can patch them. And yet most developers shipping AI features have never actually
